AI RESEARCH
On the Wasserstein Gradient Flow Interpretation of Drifting Models
arXiv CS.AI
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ArXi:2605.05118v1 Announce Type: cross Recently, Deng proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of GMD through the lens of Wasserstein Gradient Flows (WGF), i.e., the path of steepest descent for a functional in the space of probability measures, equipped with the geometry of optimal transport. Unlike previous WGF-based contributions, GMD can be thought of as directly targeting a fixed point of a specific WGF flow.